We benchmarked MixMOBO against a range of existing state-of-the-art optimization strategies that are commonly used for optimizing expensive black-box functions with mixed-variable design spaces. We chose the following single objective optimization algorithms for comparison: CoCaBO [1]}, which combines the multi-armed bandit (MAB) and Bayesian optimization approaches by using a mixing kernel. CoCaBO has been shown to be more efficient than GPyOpt (one-hot encoding approach [2]}) and EXP3BO (multi-armed bandit (MAB approach [3]}). We used CoCaBO with a mixing parameter of \(0.5\) . We also tested MixMOBO against GBRT, a sequential optimization technique using gradient boosted regression trees [4]}. TPE_Hyperopt (Tree-structured Parzen Estimator) is a sequential method for optimizing expensive black-box functions, introduced by [5]}. SMAC3 is a popular Bayesian optimization algorithm in combination with an aggressive racing mechanism [6]}. Both of these algorithms, in addition to Random Sampling, were used as baselines. Publicly available libraries for these algorithms were used.
